KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
Jiajun Shi, Jian Yang, Jiaheng Liu, Xingyuan Bu, Jiangjie Chen, Junting Zhou, Kaijing Ma, Zhoufutu Wen, Bingli Wang, Yancheng He, Liang Song, Hualei Zhu, Shilong Li, Xingjian Wang, Wei Zhang, Ruibin Yuan, Yifan Yao, Wenjun Yang, Yunli Wang, Siyuan Fang, Siyu Yuan, Qianyu He

TL;DR
KORGym is a versatile, interactive evaluation platform with over fifty games designed to assess and analyze the reasoning capabilities of large language and vision models in complex, multi-turn scenarios.
Contribution
We introduce KORGym, a novel dynamic evaluation platform with diverse interactive games for comprehensive reasoning assessment of LLMs and VLMs.
Findings
Closed-source models outperform open-source ones.
Model performance varies with modality and reasoning strategies.
Reinforcement learning impacts model reasoning abilities.
Abstract
Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchmarks are often domain-specific and thus cannot fully capture an LLM's general reasoning potential. To address this limitation, we introduce the Knowledge Orthogonal Reasoning Gymnasium (KORGym), a dynamic evaluation platform inspired by KOR-Bench and Gymnasium. KORGym offers over fifty games in either textual or visual formats and supports interactive, multi-turn assessments with reinforcement learning scenarios. Using KORGym, we conduct extensive experiments on 19 LLMs and 8 VLMs, revealing consistent reasoning patterns within model families and demonstrating the superior performance of closed-source models. Further analysis examines the effects of modality, reasoning strategies, reinforcement learning…
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Code & Models
Videos
Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI)
